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• W2054702204 abstract "The heterogeneous nature of human hepatocellular carcinoma (HCC) has hampered both treatment and prognostic predictions. Gene expression profiles of human HCC were analyzed to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. By applying global gene expression analyses, including unsupervised and supervised methods, 2 distinctive subclasses of HCC that were highly homogeneous for both the underlying biology and the clinical outcome were discovered. Tumors from the low survival subclass had strong cell proliferation and antiapoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and sumoylation, suggesting an etiologic involvement of these processes in accelerating the progression of HCC. Genes most strongly associated with survival were identified by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provided new molecular insights into the pathogenesis of HCC. Future studies will evaluate potential diagnostic markers and therapeutic targets identified during the global gene expression studies. Furthermore, cross-species similarity of gene expression patterns will also allow prioritization of a long list of genes obtained from human gene expression profiling studies and focus on genes whose expression is altered during tumorigenesis in both species. The heterogeneous nature of human hepatocellular carcinoma (HCC) has hampered both treatment and prognostic predictions. Gene expression profiles of human HCC were analyzed to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. By applying global gene expression analyses, including unsupervised and supervised methods, 2 distinctive subclasses of HCC that were highly homogeneous for both the underlying biology and the clinical outcome were discovered. Tumors from the low survival subclass had strong cell proliferation and antiapoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and sumoylation, suggesting an etiologic involvement of these processes in accelerating the progression of HCC. Genes most strongly associated with survival were identified by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provided new molecular insights into the pathogenesis of HCC. Future studies will evaluate potential diagnostic markers and therapeutic targets identified during the global gene expression studies. Furthermore, cross-species similarity of gene expression patterns will also allow prioritization of a long list of genes obtained from human gene expression profiling studies and focus on genes whose expression is altered during tumorigenesis in both species. Although much is known about both the cellular changes that lead to hepatocellular carcinoma (HCC) and the etiologic agents (ie, hepatitis B virus [HBV], hepatitis C virus [HCV], and alcohol) responsible for the majority of HCC, the molecular pathogenesis of HCC is not well understood.1Thorgeirsson S.S. Grisham J.W. Molecular pathogenesis of human hepatocellular carcinoma.Nat Genet. 2002; 31: 339-346Crossref PubMed Scopus (1261) Google Scholar Also, despite considerable progress in using clinical and pathologic diagnosis of HCC to predict patient survival and responses to therapy,2Bruix J. Llovet J.M. HCC surveillance who is the target population?.Hepatology. 2003; 37: 507-509Crossref PubMed Scopus (51) Google Scholar, 3Calvet X. Bruix J. Gines P. Bru C. Sole M. Vilana R. Rodes J. Prognostic factors of hepatocellular carcinoma in the west a multivariate analysis in 206 patients.Hepatology. 1990; 12: 753-760Crossref PubMed Scopus (225) Google Scholar, 4Chevret S. Trinchet J.C. Mathieu D. Rached A.A. Beaugrand M. Chastang C. A new prognostic classification for predicting survival in patients with hepatocellular carcinoma. Groupe d’Etude et de Traitement du Carcinome Hepatocellulaire.J Hepatol. 1999; 31: 133-141Abstract Full Text Full Text PDF PubMed Scopus (432) Google Scholar, 5Okuda K. Ohtsuki T. Obata H. Tomimatsu M. Okazaki N. Hasegawa H. Nakajima Y. Ohnishi K. Natural history of hepatocellular carcinoma and prognosis in relation to treatment. Study of 850 patients.Cancer. 1985; 56: 918-928Crossref PubMed Scopus (1773) Google Scholar, 6Pugh R.N. Murray-Lyon I.M. Dawson J.L. Pietroni M.C. Williams R. Transection of the oesophagus for bleeding oesophageal varices.Br J Surg. 1973; 60: 646-649Crossref PubMed Scopus (6669) Google Scholar, 7Tan C.K. Law N.M. Ng H.S. Machin D. Simple clinical prognostic model for hepatocellular carcinoma in developing countries and its validation.J Clin Oncol. 2003; 21: 2294-2298Crossref PubMed Scopus (53) Google Scholar, 8CLIP investigatorsA new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators.Hepatology. 1998; 28: 751-755Crossref PubMed Scopus (1184) Google Scholar a number of issues remains unresolved. For example, the natural course of early HCC is unknown, and intermediate and advanced HCC are known to be very heterogeneous. Thus, improving the classification of HCC patients would at minimum improve the application of currently available treatment modalities and at best provide new treatment strategies.Recently, microarray technologies have been used successfully to predict clinical outcome and survival, as well as to classify different types of cancer, including HCC.9Alizadeh A.A. Eisen M.B. Davis R.E. Ma C. Lossos I.S. Rosenwald A. Boldrick J.C. Sabet H. Tran T. Yu X. Powell J.I. Yang L. Marti G.E. Moore T. Hudson Jr, J. Lu L. Lewis D.B. Tibshirani R. Sherlock G. Chan W.C. Greiner T.C. Weisenburger D.D. Armitage J.O. Warnke R. Levy R. Wilson W. Grever M.R. Byrd J.C. Botstein D. Brown P.O. Staudt L.M. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature. 2000; 403: 503-511Crossref PubMed Scopus (7919) Google Scholar, 10Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1646) Google Scholar, 11Chen X. Cheung S.T. So S. Fan S.T. Barry C. Higgins J. Lai K.M. Ji J. Dudoit S. Ng I.O. van de Rijn M. Botstein D. Brown P.O. Gene expression patterns in human liver cancers.Mol Biol Cell. 2002; 13: 1929-1939Crossref PubMed Scopus (731) Google Scholar, 12Iizuka N. Oka M. Yamada-Okabe H. Nishida M. Maeda Y. Mori N. Takao T. Tamesa T. Tangoku A. Tabuchi H. Hamada K. Nakayama H. Ishitsuka H. Miyamoto T. Hirabayashi A. Uchimura S. Hamamoto Y. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection.Lancet. 2003; 361: 923-929Abstract Full Text Full Text PDF PubMed Scopus (438) Google Scholar, 13Lee J.S. Thorgeirsson S.S. Functional and genomic implications of global gene expression profiles in cell lines from human hepatocellular cancer.Hepatology. 2002; 35: 1134-1143Crossref PubMed Scopus (99) Google Scholar, 14Okabe H. Satoh S. Kato T. Kitahara O. Yanagawa R. Yamaoka Y. Tsunoda T. Furukawa Y. Nakamura Y. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray identification of genes involved in viral carcinogenesis and tumor progression.Cancer Res. 2001; 61: 2129-2137PubMed Google Scholar, 15Rosenwald A. Wright G. Chan W.C. Connors J.M. Campo E. Fisher R.I. Gascoyne R.D. Muller-Hermelink H.K. Smeland E.B. Giltnane J.M. Hurt E.M. Zhao H. Averett L. Yang L. Wilson W.H. Jaffe E.S. Simon R. Klausner R.D. Powell J. Duffey P.L. Longo D.L. Greiner T.C. Weisenburger D.D. Sanger W.G. Dave B.J. Lynch J.C. Vose J. Armitage J.O. Montserrat E. Lopez-Guillermo A. Grogan T.M. Miller T.P. LeBlanc M. Ott G. Kvaloy S. Delabie J. Holte H. Krajci P. Stokke T. Staudt L.M. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.N Engl J Med. 2002; 346: 1937-1947Crossref PubMed Scopus (3149) Google Scholar, 16Rosenwald A. Wright G. Wiestner A. Chan W.C. Connors J.M. Campo E. Gascoyne R.D. Grogan T.M. Muller-Hermelink H.K. Smeland E.B. Chiorazzi M. Giltnane J.M. Hurt E.M. Zhao H. Averett L. Henrickson S. Yang L. Powell J. Wilson W.H. Jaffe E.S. Simon R. Klausner R.D. Montserrat E. Bosch F. Greiner T.C. Weisenburger D.D. Sanger W.G. Dave B.J. Lynch J.C. Vose J. Armitage J.O. Fisher R.I. Miller T.P. LeBlanc M. Ott G. Kvaloy S. Holte H. Delabie J. Staudt L.M. The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma.Cancer Cell. 2003; 3: 185-197Abstract Full Text Full Text PDF PubMed Scopus (763) Google Scholar, 17van’t Veer L.J. Dai H. van de Vijver M.J. He Y.D. Hart A.A. Mao M. Peterse H.L. van der Kooy K. Marton M.J. Witteveen A.T. Schreiber G.J. Kerkhoven R.M. Roberts C. Linsley P.S. Bernards R. Friend S.H. Gene expression profiling predicts clinical outcome of breast cancer.Nature. 2002; 415: 530-536Crossref PubMed Scopus (7697) Google Scholar, 18Ye Q.H. Qin L.X. Forgues M. He P. Kim J.W. Peng A.C. Simon R. Li Y. Robles A.I. Chen Y. Ma Z.C. Wu Z.Q. Ye S.L. Liu Y.K. Tang Z.Y. Wang X.W. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.Nat Med. 2003; 9: 416-423Crossref PubMed Scopus (712) Google Scholar In this report, we will describe the new findings in molecular diagnosis and prediction of clinical outcomes of HCC using gene expression profiling technology as well as provide a prospective view on how the information gathered using this new technology can be used in the development of effective therapeutic interventions for HCC. Our current study revealed 2 subclasses of HCC patients characterized by significant differences in the length of survival. Additionally, expression profiles of a limited number of genes that accurately predicted the length of survival were identified. Thus, application of gene expression patterns can accurately predict the clinical outcome of HCC at the time of diagnosis.Identification of 2 distinct subclasses of HCC highly associated with survival of patientsGene expression profiles of 91 human HCCs were obtained from experiments using DNA microarrays containing 21,329 unique genes. Total RNA were used to prepare fluorescent cDNA probes labeled with the Cy3 or Cy5 dyes. A reference fluorescent cDNA probe was prepared from pooled total RNA from 19 normal human livers. To minimize labeling biases, total RNA from each tissue were labeled with the reciprocal fluorochrome in duplicated experiments. Because the replicates showed high correlation coefficients and reliable reproducibility, expression ratios of each gene were averaged from replicated experiments and used in subsequent analysis. After initial reduction to focus on those genes whose expression varied nontrivially across tissues, a hierarchical clustering analysis based on Pearson correlation coefficients was applied to all tissues on the basis of similarity in the expression pattern over all genes. This analysis revealed 2 distinctive subtypes of gene expression patterns among 91 HCCs (Figure 1A) . Members of the 2 clusters also resided in compact and easily separable 3-dimensional space when viewed by principal component analysis or a 3-dimensional multidimensional scaling plot based on their overall similarity of expression patterns.Among all clinical data of HCC patients examined, a significant association with the clusters was only detected in patient survival. The overall survival time in cluster A (30.3 ± 8.02 months) was shorter than cluster B (83.7 ± 10.3 months). As expected, the Kaplan–Meier survival curve and log-rank test indicated poorer survival in cluster A patients (P < 1.0 × 10−4) when compared with cluster B (Figure 1B). Thus, the molecular differences between these 2 subclasses of HCC were associated with a remarkable difference in the clinical outcome of these patients.Prediction of survival with gene expression profilesFive different statistical methods were used to determine whether gene expression patterns could be used to predict survival: linear discriminator analysis (LDA), support vector machines (SVM), nearest centroid (NC), nearest neighbor (NN), and compound covariate predictor (CCP). To assess the validation of the results and reproducibility of the test, the HCCs were randomly divided into 2 equal groups: training set (n = 45) that was used to develop the HCC classifiers and validation set (n = 44) that was used to evaluate the test (Figure 2). Briefly, we started by identifying the most differentially expressed genes between 2 clusters in the training set. These genes were combined to form a series of classifiers that estimated the probability that a particular HCC belonged to cluster A or B. The number of genes in the classifiers was optimized to minimize misclassification errors during the leave-one-out cross validation of the tumors in the training set. When applied to the validation set, all 5 models successfully separated poorer survival patients (cluster A) from longer survival patients (cluster B). All Kaplan–Meier survival curves and log-rank tests in the validation set showed significant differences between subclass A and B that were independently predicted by the 6 classifier models. These results demonstrated not only a strong association of gene expression patterns with the survival of the patients but also a robust reproducibility of these gene expression-based predictors.Figure 2Schematic diagram of predicting patient survival by training various classifiers. The class predictions were carried out using the cross-validation approach and 5 different algorithms, linear discriminator analysis (LDA), support vector machines (SVM), nearest centroid (NC), nearest neighbor (NN), and compound covariate predictor (CCP). Before applying prediction models, HCC tissues were randomly divided into 2 equal groups, a training set and a validation set, to estimate the reproducibility of the tests. The training and validation sets consist of 45 and 44 HCC tissues, respectively. Each predictor independently identified the most differentially expressed genes between 2 clusters in the training set. Collected genes were then used to build prediction models that can estimate the probability that a given HCC tissue was cluster A or B. To minimize the misclassification error, numbers of genes in each predictor were optimized during leave-one-out cross-validation in training set tissues. During cross-validation, one tissue was removed at a time from the training set, and the remaining tissues were used to build prediction models. The identity of the left-out tissue was predicted based on given algorithm of predictors. Cross-validation was repeated until each tissue in the training set had been left out once. After applying 6 predictors to the validation set, Kaplan–Meier survival plots and log-rank test were used to access statistical significance of predicted groups in survival.View Large Image Figure ViewerDownload (PPT)Identification of survival genes and gene expression signatures of survival genes in independent data setsTo identify the genes whose expressions were most associated with the survival of HCC patients, the univariate Cox proportional hazards model was used. Expression of 406 genes was highly correlated with length of survival with strong statistical significance (P < .001). The outcome of hierarchical cluster analysis of the HCC with the 406 survival genes was similar to the previous analysis with all the genes. With few exceptions, cluster memberships of each tumor remained the same in the 2 hierarchical cluster dendrograms, highlighting again the robustness of the predicted HCC subclasses and their association with the length of survival. Taken together with the cross-validation test of training and validation data sets, these results further supported the notion that a distinct gene expression pattern predicted survival characteristics of the 2 subclasses of the HCC patients.To assess the generality of the 2 HCC subclasses and the corresponding gene expression signatures, we compared our data with previously published data. Of the 406 survival genes identified in the present study, 207, 163, and 210 genes were represented in HCC gene expression data from studies of Chen et al,11Chen X. Cheung S.T. So S. Fan S.T. Barry C. Higgins J. Lai K.M. Ji J. Dudoit S. Ng I.O. van de Rijn M. Botstein D. Brown P.O. Gene expression patterns in human liver cancers.Mol Biol Cell. 2002; 13: 1929-1939Crossref PubMed Scopus (731) Google Scholar Ye et al,18Ye Q.H. Qin L.X. Forgues M. He P. Kim J.W. Peng A.C. Simon R. Li Y. Robles A.I. Chen Y. Ma Z.C. Wu Z.Q. Ye S.L. Liu Y.K. Tang Z.Y. Wang X.W. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.Nat Med. 2003; 9: 416-423Crossref PubMed Scopus (712) Google Scholar and Iizuka et al,12Iizuka N. Oka M. Yamada-Okabe H. Nishida M. Maeda Y. Mori N. Takao T. Tamesa T. Tangoku A. Tabuchi H. Hamada K. Nakayama H. Ishitsuka H. Miyamoto T. Hirabayashi A. Uchimura S. Hamamoto Y. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection.Lancet. 2003; 361: 923-929Abstract Full Text Full Text PDF PubMed Scopus (438) Google Scholar respectively. Hierarchical cluster analysis of 3 independent data sets with overlapped survival genes also disclosed 2 distinctive subclasses of HCC, clearly demonstrating that the 2 subtypes of HCC discovered were not due to errors in tissue sampling or statistical models applied.Conclusion and perspectivesPrognostic modeling of patients with HCC at diagnosis that considers tumor stage, functional impairment of the liver, and general condition of the patient can provide valuable information and guide therapy.2Bruix J. Llovet J.M. HCC surveillance who is the target population?.Hepatology. 2003; 37: 507-509Crossref PubMed Scopus (51) Google Scholar, 8CLIP investigatorsA new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators.Hepatology. 1998; 28: 751-755Crossref PubMed Scopus (1184) Google Scholar However, increased surveillance and advances in image technology have afforded earlier diagnosis of HCC. This development presents a challenge with respect to prognostic modeling of HCC because the natural history of early HCC is unknown.19Llovet J.M. Bru C. Bruix J. Prognosis of hepatocellular carcinoma the BCLC staging classification.Semin Liver Dis. 1999; 19: 329-338Crossref PubMed Scopus (2877) Google Scholar In addition, intermediate and advanced HCC are quite heterogeneous,20Llovet J.M. Bustamante J. Castells A. Vilana R. Ayuso Mdel C. Sala M. Bru C. Rodes J. Bruix J. Natural history of untreated nonsurgical hepatocellular carcinoma rationale for the design and evaluation of therapeutic trials.Hepatology. 1999; 29: 62-67Crossref PubMed Scopus (988) Google Scholar even though the natural history and prognostic factors are welldefined.19Llovet J.M. Bru C. Bruix J. Prognosis of hepatocellular carcinoma the BCLC staging classification.Semin Liver Dis. 1999; 19: 329-338Crossref PubMed Scopus (2877) Google Scholar Therefore, it is necessary to establish robust methods capable of evaluating the prognosis of patients diagnosed at early, intermediate, and late stages of HCC. As a first step in the development of a molecular prognostic evaluation, we have used gene expression profiling technology and unsupervised and supervised learning methods to predict successfully the survival of HCC patients.Hierarchical clustering based solely on gene expression patterns uncovered 2 subclasses of HCC strongly associated with the length of patient survival. Subsequent supervised analyses including leave-one-out cross-validation of survival prediction, and a univariate Cox regression model validated the presence of 2 subclasses of HCC. Moreover, information obtained from knowledge-based annotation of the 406 survival genes provided insight into the underlying biologic differences between the 2 subclasses of HCC. Out of several biologic groups of the survival genes, the cell proliferation group was the best predictor of an unfavorable outcome of the disease. It was consistent with previous analyses in human lymphomas.15Rosenwald A. Wright G. Chan W.C. Connors J.M. Campo E. Fisher R.I. Gascoyne R.D. Muller-Hermelink H.K. Smeland E.B. Giltnane J.M. Hurt E.M. Zhao H. Averett L. Yang L. Wilson W.H. Jaffe E.S. Simon R. Klausner R.D. Powell J. Duffey P.L. Longo D.L. Greiner T.C. Weisenburger D.D. Sanger W.G. Dave B.J. Lynch J.C. Vose J. Armitage J.O. Montserrat E. Lopez-Guillermo A. Grogan T.M. Miller T.P. LeBlanc M. Ott G. Kvaloy S. Delabie J. Holte H. Krajci P. Stokke T. Staudt L.M. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.N Engl J Med. 2002; 346: 1937-1947Crossref PubMed Scopus (3149) Google Scholar Expression of typical cell proliferation markers such as PNCA and cell cycle regulators such as CDK4, CCNB1, CCNA2, and CKS2 was greater in subclass A than subclass B. Not surprisingly, many genes that were expressed more in subclass A were antiapoptotic. Interestingly, higher expression of genes involved in ubiquitination and sumoylation was also observed. The ubiquitin system is often deregulated in cancer.21Pagano M. Benmaamar R. When protein destruction runs amok, malignancy is on the loose.Cancer Cell. 2003; 4: 251-256Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar In HCC, the degree of ubiquitination is highly correlated with cell proliferation and survival of patients and has also been proposed as a possible predictive marker for recurrence of human HCC.22Shirahashi H. Sakaida I. Terai S. Hironaka K. Kusano N. Okita K. Ubiquitin is a possible new predictive marker for the recurrence of human hepatocellular carcinoma.Liver. 2002; 22: 413-418Crossref PubMed Scopus (21) Google Scholar In addition, PSMD10/Gankyrin, a subunit of the 26S proteasome that accelerates the degradation of RB, is over expressed in HCC.23Higashitsuji H. Itoh K. Nagao T. Dawson S. Nonoguchi K. Kido T. Mayer R.J. Arii S. Fujita J. Reduced stability of retinoblastoma protein by gankyrin, an oncogenic ankyrin-repeat protein over-expressed in hepatomas.Nat Med. 2000; 6: 96-99Crossref PubMed Scopus (278) Google Scholar Also, enhanced activation of ubiquitin-dependent protein degradation may account for deregulation of cell cycle control and faster cell proliferation in the poor survival group (subclass A). Therefore, deregulated components in ubiquitin-mediated protein degradation may provide attractive therapeutic targets for novel HCC treatment modalities.Although the 2 subclasses of HCC may be viewed as distinctive biologic entities, they still shared significant overall similarity of gene expression when compared with surrounding nontumorous tissue (ST). This similarity may indicate that HCCs in subclass A have, on top of a common HCC gene expression signature, accumulated additional oncogenic alterations that are reflected in the gene expression profile, providing a more favorable environment for tumor growth. However, we cannot rule out the possibility that different mechanisms contribute to the development of subclass A and B, such as exposure to different etiologic factors, and that the gene expression signature reflects the etiologic “footprint.” Alternatively, the cell of origin of a tumor may be important in determining the clinical outcome, as shown for diffuse large B-cell lymphoma.9Alizadeh A.A. Eisen M.B. Davis R.E. Ma C. Lossos I.S. Rosenwald A. Boldrick J.C. Sabet H. Tran T. Yu X. Powell J.I. Yang L. Marti G.E. Moore T. Hudson Jr, J. Lu L. Lewis D.B. Tibshirani R. Sherlock G. Chan W.C. Greiner T.C. Weisenburger D.D. Armitage J.O. Warnke R. Levy R. Wilson W. Grever M.R. Byrd J.C. Botstein D. Brown P.O. Staudt L.M. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature. 2000; 403: 503-511Crossref PubMed Scopus (7919) Google Scholar It is therefore possible that the 2 subclasses of HCC represent different cellular origins (ie, hepatic stem cells vs. hepatocytes) of the tumors.The severity of HCC and the lack of good diagnostic markers and treatment strategies have rendered the disease a major challenge. Systematic analysis of gene expression patterns provides an insight into the biology and pathogenesis of HCC. New results indicate that HCC prognosis can be predicted from the gene expression profiles of the primary tumors. Because the microarray-based measurement of gene expression reflects the abundance of expressed mRNA and proteins in the HCC, a limited set of quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) and/or immunohistochemical staining assays may be sufficient to predict the prognosis of patients at the time of diagnosis. Further studies need to explore the database generated by the genomic scale gene expression studies of HCC to identify potential therapeutic targets.The application of animal models provides the opportunity to analyze the multiple-step tumor progression of HCC serially. Moreover, cross-species similarity of gene expression patterns allows prioritization of the long list of genes obtained from human gene expression profiling studies and focus on genes whose expression is altered during tumorigenesis in both species. This approach might facilitate the discovery of tumorigenic mechanisms and/or pathways that are evolutionary conserved. Furthermore, cross-species comparisons, comparative functional genomics, of HCC gene expression patterns can identify the most appropriate mouse cancer models to study human HCC. Identification of such mouse models will be invaluable for testing hypotheses derived from various genomic scale studies and to evaluate potential therapeutic targets or drug candidates (Figure 3). Finally, establishing this molecular relationship between the mouse cancer models and human cancers may provide an opportunity to explore the relevance of the earliest events in the mouse models for treatment and prevention of human cancer.Figure 3Comparative functional oncogenomics. Gene expression data of mouse and human HCC tissues are collected independently in microarray experiments. Before integration of 2 independent data sets, orthologous genes that are present in both microarrays are selected for further analysis. By applying unsupervised (hierarchical clustering) and supervised analysis (prediction models) of gene expression patterns, mouse models that best or least mimic human conditions can be identified. Identified mouse models will be used to test hypotheses on tumor progression that are generated during cross-species gene expression pattern analysis or from other experimental data. These models will be also extremely valuable to test the potential therapeutic targets identified in human study and preclinical trials of drugs.View Large Image Figure ViewerDownload (PPT) Although much is known about both the cellular changes that lead to hepatocellular carcinoma (HCC) and the etiologic agents (ie, hepatitis B virus [HBV], hepatitis C virus [HCV], and alcohol) responsible for the majority of HCC, the molecular pathogenesis of HCC is not well understood.1Thorgeirsson S.S. Grisham J.W. Molecular pathogenesis of human hepatocellular carcinoma.Nat Genet. 2002; 31: 339-346Crossref PubMed Scopus (1261) Google Scholar Also, despite considerable progress in using clinical and pathologic diagnosis of HCC to predict patient survival and responses to therapy,2Bruix J. Llovet J.M. HCC surveillance who is the target population?.Hepatology. 2003; 37: 507-509Crossref PubMed Scopus (51) Google Scholar, 3Calvet X. Bruix J. Gines P. Bru C. Sole M. Vilana R. Rodes J. Prognostic factors of hepatocellular carcinoma in the west a multivariate analysis in 206 patients.Hepatology. 1990; 12: 753-760Crossref PubMed Scopus (225) Google Scholar, 4Chevret S. Trinchet J.C. Mathieu D. Rached A.A. Beaugrand M. Chastang C. A new prognostic classification for predicting survival in patients with hepatocellular carcinoma. Groupe d’Etude et de Traitement du Carcinome Hepatocellulaire.J Hepatol. 1999; 31: 133-141Abstract Full Text Full Text PDF PubMed Scopus (432) Google Scholar, 5Okuda K. Ohtsuki T. Obata H. Tomimatsu M. Okazaki N. Hasegawa H. Nakajima Y. Ohnishi K. Natural history of hepatocellular carcinoma and prognosis in relation to treatment. Study of 850 patients.Cancer. 1985; 56: 918-928Crossref PubMed Scopus (1773) Google Scholar, 6Pugh R.N. Murray-Lyon I.M. Dawson J.L. Pietroni M.C. Williams R. Transection of the oesophagus for bleeding oesophageal varices.Br J Surg. 1973; 60: 646-649Crossref PubMed Scopus (6669) Google Scholar, 7Tan C.K. Law N.M. Ng H.S. Machin D. Simple clinical prognostic model for hepatocellular carcinoma in developing countries and its validation.J Clin Oncol. 2003; 21: 2294-2298Crossref PubMed Scopus (53) Google Scholar, 8CLIP investigatorsA new prognostic system for hepatoc" @default.
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• W2054702204 title "Genome-scale profiling of gene expression in hepatocellular carcinoma: Classification, survival prediction, and identification of therapeutic targets" @default.
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